IEEE Trans Vis Comput Graph - Adaptive Motion Data Representation with Repeated Motion Analysis.

Tópicos

{ motion(1329) object(1292) video(1091) }
{ imag(1947) propos(1133) code(1026) }
{ cost(1906) reduc(1198) effect(832) }
{ perform(999) metric(946) measur(919) }
{ concept(1167) ontolog(924) domain(897) }
{ state(1844) use(1261) util(961) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ featur(1941) imag(1645) propos(1176) }
{ model(2341) predict(2261) use(1141) }
{ use(976) code(926) identifi(902) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ patient(2837) hospit(1953) medic(668) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
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{ data(1737) use(1416) pattern(1282) }
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{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
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{ extract(1171) text(1153) clinic(932) }
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{ care(1570) inform(1187) nurs(1089) }
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{ method(984) reconstruct(947) comput(926) }
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{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
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{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this paper, we present a representation method for motion capture data by exploiting the nearly repeated characteristics and spatio-temporal coherence in human motion. We extract similar motion clips of variable lengths or speeds across the database. Since the coding costs between these matched clips are small, we propose the repeated motion analysis to extract the refered and repeated clip pairs with maximum compression gains. For further utilization of motion coherence, we approximate the subspace-projected clip motions or residuals by interpolated functions with range-aware adaptive quantization. Our experiments demonstrate that the proposed feature-aware method is of high computational efficiency. Furthermore, it also provides substantial compression gains with comparable reconstruction and perceptual errors.

Resumo Limpo

paper present represent method motion captur data exploit near repeat characterist spatiotempor coher human motion extract similar motion clip variabl length speed across databas sinc code cost match clip small propos repeat motion analysi extract refer repeat clip pair maximum compress gain util motion coher approxim subspaceproject clip motion residu interpol function rangeawar adapt quantiz experi demonstr propos featureawar method high comput effici furthermor also provid substanti compress gain compar reconstruct perceptu error

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